With the recent increase in the number of subscribers for mobile communication services, the expansion of the capacity of cells covered by each wireless base station has been demanded. One of the multiple access technologies that will help solve this problem is a technology called Space Division Multiple Access, or “SDMA” for short.
SDMA is a method used by a wireless base station to communicate with each of a plurality of mobile stations at the same timeslot and frequency, by dividing a communication space. The division of the communication space is realized when the wireless base station forms an optimum directivity pattern separately for each individual mobile station. A directivity pattern shows the direction in which signals are transmitted or received, and the intensity at which those signals are transmitted or received.
The wireless base station is equipped with an adaptive array apparatus as a device for forming directivity patterns. The adaptive array apparatus is chiefly composed of a plurality of antennas and a DSP (Digital Signal Processor), and forms any directivity patterns freely by weighting with particular values the amplitude and phase of each signal transmitted or received by each antenna. The adaptive array apparatus then performs transmission and reception of signals using the formed patterns. Hereafter, a value used for weighting is referred to as a weight signal, and a set of weight signals for forming a directivity pattern is referred to as a weight vector.
The adaptive array apparatus forms an optimal directivity pattern for a mobile station, by tracking the movements of the mobile station, which sends signals in a direction unforeknown to the adaptive array apparatus. One of the principles on which the adaptive array apparatus operates is MMSE (Minimum Mean Square Error). MMSE requires reference signals. A reference signal serves as a target when weighting received signals and combining them to produce a signal. An adaptive array apparatus that uses MMSE determines a weight vector so as to minimize a difference between the reference signal and the combined signal. Using such a weight vector creates a directivity pattern which is optimal for receiving signals from the mobile station.
In TDMA/TDD (Time Division Multiple Access/Time Division Duplex), which is another multiple access technique, each timeslot includes predetermined bit patterns such as a preamble and a unique word at the top and a payload in the following section.
This means that when a combination of SDMA, MMSE and TDMA/TDD is used in communication, the adaptive array apparatus can use the preamble, the unique word and the like as reference signals.
In more detail, the adaptive array apparatus sets a certain weight vector as an initial value, and compares a reference signal, such as a preamble and a unique word, with a signal that is obtained by weighting using the weight vector. In order to minimize the difference between them, the adaptive array apparatus adjusts the value of the weight vector. By repeating such processing for a bit pattern in symbol units, the weight vector converges on a certain value after a period of time. Following this, signals of the payload is weighted by the converged weight vector and then extracted. After receiving the predetermined bit pattern such as the preamble and the unique word, the adaptive array apparatus identifies the extracted signals and uses them as reference signals on the premise that the identification is correct.
However, the conventional adaptive array apparatus described above has problems in that a weight vector converges slowly and unstably. Here, converging unstable means that the value of a weight vector fluctuates and does not settle at a certain value. Converging slowly means that it takes long from the setting of an initial value of a weight vector to the convergence of the weight vector.
One reason is that in an algorithm of calculating weight vectors, stability in convergence is an element opposing to convergence speed. Therefore, it is difficult to improve both of them. For instance, in LMS (Least Mean Square), a typical algorithm for MMSE, increasing the speed of convergence leads to deterioration in convergence stability, whereas an improvement in stability slows the convergence.
Another reason is associated with the way of setting an initial value of a weight vector. Since the initial value is fixed in the conventional adaptive array apparatus, it takes long for the weight vector value to converge on an optimal value. In fact, in many cases weight vectors cannot converge sufficiently.
FIGS. 6A and 6B show difference curves. In the actual fact, a weight vector is multidimensional and so the difference curve has a curved surface in a multidimensional space, though the weight vector is illustrated as a one-dimensional vector here for the sake of convenience. In LMS algorithm and RLS (Recursive Least-Squares) algorithm which is also used for MMSE, the weight vector is adjusted consecutively in a direction that decreases the value of the difference curve.
As shown in FIG. 6A, it does not take long for an initial value Winit (A) to converge on an optimal value Wopt, as it is close to Wopt. In contrast, it takes long for an initial value Winit (B) to converge, as it is not close to Wopt. If only a single fixed initial value is used, it takes long for the initial value to converge, as it is not close to an optimal value.
If a weight vector calculated from the preceding symbol is set as an initial value, however, there is a high probability of the weight vector value being a local minimum value, because it is the point where the weight vector has finally settled as a result of an adjustment to minimize the difference.
As shown in FIG. 6B, it is known by experience that Winit may be a local minimum value due to noise or other factors. In that case, it is impossible for the weight vector value to pass a peak and converge on an optimal value Wopt which is a general minimum value.
The use of RLS can increase convergence speed, but when an error exists in signals at the beginning, the error is aggravated with successive calculations. This means that if there is a major error in a weight vector calculated from the last symbol, there is a danger that a totally wrong weight vector may be produced in the following reception period.